New technologies such as Internet, cloud computing, artificial intelligence (AI) and blockchain have greatly promoted the innovation of financial industry structure and paradigm, improved the efficiency of financial services and brought the spillover of the financial technology (FinTech) risk. The existing financial regulation methods cannot meet the needs of the development of FinTech, therefore, there is an urgent need to improve the trust mechanism. Blockchain can effectively solve the problems of security and trust in FinTech. This paper will explore the expression method of trust index in blockchain, and build based on blockchain a multi-dimensional trust index system and evaluation mechanism (MDTEM) for FinTech. Firstly, a four-level blockchain structure has been built, including cloud level blockchain, Internet level blockchain, contract level blockchain and application level blockchain in the FinTech ecological environment to ensure the security, reliability and trustworthiness of financial services. Secondly, according to the trust structure of blockchain on FinTech payment behavior, the index system and evaluation mechanism of direct trust, indirect trust, recommendation trust and feedback trust of FinTech based on blockchain are designed. Finally, the trust simulation experiment of FinTech mechanism is carried out from three aspects: data sending, data transmission, data reception and delivery success rate. Simulation results show that the proposed MDTEM trust mechanism can better improve the safe and reliable application of FinTech trust mechanism.

The prerequisite for the use of remote sensing images is that their security must be guaranteed. As a special subset of perceptual hashing, subject-sensitive hashing overcomes the shortcomings of the existing perceptual hashing that cannot distinguish between “subject-related tampering” and “subject-unrelated tampering” of remote sensing images. However, the existing subject-sensitive hashing still has a large deficiency in robustness. In this paper, we propose a novel attention-based asymmetric U-Net (AAU-Net) for the subject-sensitive hashing of remote sensing (RS) images. Our AAU-Net demonstrates obvious asymmetric structure characteristics, which is important to improve the robustness of features by combining the attention mechanism and the characteristics of subject-sensitive hashing. On the basis of AAU-Net, a subject-sensitive hashing algorithm is developed to integrate the features of various bands of RS images. Our experimental results show that our AAU-Net-based subject-sensitive hashing algorithm is more robust than the existing deep learning models such as Attention U-Net and MUM-Net, and its tampering sensitivity remains at the same level as that of Attention U-Net and MUM-Net.

The implicit prerequisite for using HRRS images is that the images can be trusted. Otherwise, their value would be greatly reduced. As a new data security technology, subject-sensitive hashing overcomes the shortcomings of existing integrity authentication methods and could realize subject-sensitive authentication of HRRS images. However, shortcomings of the existing algorithm, in terms of robustness, limit its application. For example, the lack of robustness against JPEG compression makes existing algorithms more passive in some applications. To enhance the robustness, we proposed a Transformer-based subject-sensitive hashing algorithm. In this paper, first, we designed a Transformer-based HRRS image feature extraction network by improving Swin-Unet. Next, subject-sensitive features of HRRS images were extracted by this improved Swin-Unet. Then, the hash sequence was generated through a feature coding method that combined mapping mechanisms with principal component analysis (PCA). Our experimental results showed that the robustness of the proposed algorithm was greatly improved in comparison with existing algorithms, especially the robustness against JPEG compression.

As a new integrity authentication technology, subject-sensitive hashing has the ability to achieve subject-sensitive authentication for high-resolution remote sensing (HRRS) images and can provide a security guarantee for their subsequent use. However, existing research on subject-sensitive hashing focuses on improving the structure of the deep neural network of the algorithm to improve the algorithm's performance, which makes it necessary to reconstruct the training dataset or modify the network structure in the face of different integrity authentication requirements. In this paper, we delve into the impact of dropout on subject-sensitive hashing and propose a stepwise-drop mechanism to address the robustness and tampering-sensitivity requirements of subject-sensitive hashing. On this basis, a network named Stepwise-drop and Transformer based U-net (SDTU-net) is proposed for subject-sensitive hashing of HRRS images. SDTU-net can use our proposed stepwise-drop mechanism to determine the drop rate of different network layers, which makes it possible to adjust the algorithm performance without changing network structure and training data. Experiments show that our SDTU-net based subject-sensitive hashing has better overall performance compared with existing algorithms, especially at medium and low thresholds. Our approach solves the problem that the existing algorithms cannot balance robustness and tamper sensitivity at low thresholds.

Automatic guided vehicle (AGV) systems are widely used, and their importance for material handling is expected to grow rapidly. The advantages that such systems can offer include increased flexibility, better space utilisation, improved factory floor safety, reduction in overall operating costs, and easy interfacing with other automated systems. In this study, the authors design a hardware environment, software environment and an optimised evaluation model for large‐scale AGV scheduling systems. This magnetic navigation AGV system scheduling method can be used for production workshops and warehouses, and between production workshops and warehouses, and it improves the efficiency and output of production logistics, reduces the burden of staff and saves on enterprise costs.